Abstract 201: Time is Brain: Results From the Escape Randomized Controlled Trial
Bibliographic record
Abstract
Introduction: The ESCAPE trial used innovative imaging and aggressive target time metrics to demonstrate the benefit of additional endovascular treatment over standard care in patients with disabling acute ischemic stroke. We analyze the impact of time from onset to reperfusion and from imaging to reperfusion on clinical outcome. Methods: The trial enrolled 316 patients fulfilling eligibility criteria and presenting within 12 hours of stroke symptom onset from 22 sites across 3 continents between February 2013 and October 2014. Logistic regression models were used to estimate the probability of functionally independent outcome (modified Rankin Scale 0-2 at 90 days) based on time from stroke symptom onset to qualifying CT, stroke symptom onset to first reperfusion and qualifying CT to reperfusion after adjusting for age, sex, baseline NIHSS, occlusion site, baseline Alberta Stroke Program Early CT Score (ASPECTS), intravenous alteplase administration (and time from stroke symptom onset to qualifying CT when the predictor time variable was time from qualifying CT to reperfusion). Results: A 30-minute increase in time from qualifying CT to reperfusion decreases the probability of functionally independent outcome (mRS 0-2 at 90 days) by an absolute reduction of 8.5% (p=0.006). Similar trends in relationship between outcome and time from qualifying CT to reperfusion were noted for mRS cut-points 0-1 vs. 2-6 (p=0.08) and 0-3 vs. 4-6 (p=0.04). There was no relationship between clinical outcome and stroke symptom onset to qualifying CT for any mRS cut-point. A modest relationship was noted between stroke symptom onset to reperfusion time and the probability of achieving functionally independent outcome (mRS 0-2 vs. 3-6) (p = 0.04). Conclusions and Relevance: The ESCAPE trial data with imaging based selection reveals that imaging-to-reperfusion time is more important than onset-to-imaging time as a predictor of outcome. Trial Registration at clinicaltrials.gov NCT01778335
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".